Numetiva

Semiconductor engineering

A wafer of data.
A clearer picture.

See measurements in physical space. Connect the die, parameter and wafer with the patterns that deserve a closer look.

Explore the wafer views
Wafer measurementsNUMETIVA
Oxide-thickness wafer map with 997 finite oxide-thickness measurements and three unmeasured dies across physical X and Y coordinates.

Multi-wafer exploration / application scene views

Keep every wafer
in the picture.

See separate wafer identities on a shared canvas. Move between die maps and contour views while preserving the coordinate and parameter context.

Four wafers. One view.NUMETIVA
Numetiva saved-scene vector view: four-wafer map with distinct wafer identities, physical X and Y coordinates and categorical die colours.

Keep the context

One die.
Part of a larger story.

A useful map connects the spatial pattern with the measurement and its place in the process.

  1. 01

    Wafer and die identity.

    Keep wafer identities with their die X/Y positions when exploring multiple maps.

  2. 02

    Parameters in view.

    Explore measured values, status and parameter context in the appropriate map view.

  3. 03

    Connect space and variation.

    Move from the wafer to a zone comparison, a distribution or a relationship between measurements.

Variation by wafer zoneNUMETIVA
Python-generated box plots comparing oxide-thickness measurements by wafer zone.

Beyond the map

The next view
is part of the insight.

Compare zones, inspect distributions and connect a spatial observation with the rest of the engineering data.

Explore the analysis workbench

Bring your engineering question.

Explore how Numetiva fits your data, workflow and review requirements.

Talk to Numetiva
Numetiva / vector chart
100%Open SVG ↗

Zoom for individual dies and labels. Drag or scroll to pan. + / - to zoom; 0 to fit.

Numetiva / interactive product tour

From a question to a checked output

Your question.
A reviewable script.

Use the assistant to draft Python, inspect the code in the workbench, then run it against your worksheet. Bring the latest error into Review / fix when the script needs attention.

  1. 01
    Ask with context.

    Name the response, predictors and the chart you need.

  2. 02
    Inspect the draft.

    Check the column names, transformations and method.

  3. 03
    Run and review.

    Use the returned output and errors to guide the next step.

Explore AI & Python
Python / exampleWorksheet context
import numpy as np
import matplotlib.pyplot as plt

x = df["Tool_Wear_um"].to_numpy()
y = df["Vibration_RMS_g"].to_numpy()
valid = np.isfinite(x) & np.isfinite(y)
x, y = x[valid], y[valid]

fig, ax = plt.subplots()
ax.scatter(x, y, s=18, alpha=0.7)
coefficients = np.polyfit(x, y, 2)
grid = np.linspace(x.min(), x.max(), 200)
ax.plot(grid, np.polyval(coefficients, grid))
ax.set(xlabel="Tool wear / µm",
       ylabel="Vibration / g")
plt.show()
About this product tour